Protocol for subtomogram averaging of helical filaments in cryo-electron tomography

Summary Helical filaments are essential macromolecular elements in cellular organization and dynamics. Recent advances in cryo-electron tomography allow faithful imaging of isolated or in-cell filaments. Here, we present a protocol to generate density maps at sub-nanometer resolution of helical filaments by subtomogram averaging, exemplified with isolated mumps virus nucleocapsids and their in-cell form as an extension of the protocol. We detail procedures from pre-processing of tilt-series movie frames to refinement of reconstructed averages for streamlined data processing of helical filaments. For complete details on the use and execution of this protocol, please refer to Zhang et al.1

a. Glow-discharge holey Quantifoil grids overlaid with an additional layer of continuous carbon (R2/ 1 + 2 nm C, Cu 200 mesh grid, Quantifoil Micro Tools) at the front side for 45 s with a PELCO easiGlow Glow Discharge Cleaning System (set at 0.39 mBar, 15 mA, with air as atmosphere).
Note: The additional carbon layer provides more uniformly oriented horizontal filaments on the grid, which helps in obtaining uniform ice thickness and in tracing the filaments in later steps.
b. Concentrate Protein-A, 10-nm colloidal gold fiducials (Electron Microscopy Sciences) before applying to the grids.i. Pellet gold fiducials at 13,000 3 g, 4 C for 20 min.
ii. Remove the supernatant carefully and resuspend the fiducials pellet in a 3:1 volume ratio with the same buffer used for nucleocapsids preparation.
Note: In our experiments, addition of 1.5 mL of 3 3 concentrated fiducials to every 3 mL of nucleocapsid suspension per grid resulted in a low number of fiducials within the field of view of the tilt series.Therefore, tilt-series alignment was eventually performed with patch tracking (see sections below).Optimization of fiducial number might be beneficial for alignment of tilt series with lower signal-to-noise acquired on thicker specimens. 2Set a Vitrobot Mark IV plunge freezer (Thermo Fisher Scientific) to 22 C, 100% humidity, blot force of 0, blot time of 2 s and drain time of 2 s, both sides blotting with filter paper.d.Store the frozen grids in sealed boxes in liquid nitrogen until imaging.

Cryo-electron tomography data acquisition and computational setup
Timing: 10-20 h

STEP-BY-STEP METHOD DETAILS
In the following, we provide a step-by-step guide for the generation of medium to high resolution electron microscopy (EM) density maps of helical filaments by subtomogram averaging (Figure 1).This includes preprocessing of tilt-series frame movies, tilt-series alignment, CTF fitting, tomogram reconstruction, filament tracing, subtomogram generation, subtomogram alignment, averaging and classification at high to low data binning, multi-particle refinement and finally EM map evaluation.To illustrate these steps, we use the example dataset of mumps virus nucleocapsids isolated from HeLa cells following a 1-h stress treatment. 1The data comprises 20 tilt series, each containing 1-2 filaments, acquired at a pixel size of 1.69 A ˚(EMPIAR: 10751).Generally, we recommend to create separate working directories for each major step and subdirectories as mentioned in the sub-steps.c.Launch Warp: execute the Warp.exe in the installation directory of Warp and add a desktop shortcut for future use.d.Set up input parameters for processing (Figure 2).
i.Under the Input section, first locate the input ''frames'' folder.Then choose *.tif to select the TIF option, set the pixel dimensions as 3708 3 3838 px (for K2 data; px is used as an abbreviation of pixels in Warp/M while pix is used elsewhere throughout this protocol), and select int16 to match the raw, unbinned frame images.Set the pixel X/Y as 1.69 A ˚according to the unbinned pixel size of the data.Leave the Bin and Dose as default: 0.003 (unbinned) and 0.00 e/A ˚2/frame.ii.Under the Preprocessing section, set the correct path to the gain reference file (.dm file in this case; this needs the ''Flip Y axis'' operation).CRITICAL: For the example dataset, additionally select ''Flip X axis'' and ''Transpose'' because the raw images were saved as ''r/f = 3'' in relation to the gain reference image (read from the header of the tif images; equivalent to 270 counterclockwise rotation of frame images relative to the gain reference image).Warp version 1.0.9 also allows input of a *.txt or *.tif defect map file to correct defects in camera sensors, which will be applied along with the gain correction.Troubleshooting 1.
Figure 2. Screenshot of the Warp image pre-processing graphical user interface (GUI) and parameters used for processing of the example data comprising 20 tilt series (raw micrographs in EMPIAR: 10751) Initially, 23 tilt series acquired during a single microscopy session were processed (shown in this screenshot), 3 of which were not processed further as they did not contain filaments.
iii.Under the CTF part of the Preprocessing section, set the following parameters: Set the power spectrum size in the Window value as 512 px (a relatively small window).Select the Use Movie Sum option as the fitting usually works better with the power spectrum of the movie average given the low dose per frame image in tilt-series data.
Set the Voltage and Cs (spherical aberration) values as 300 kV and 2.70 mm respectively based on the microscope configuration.
Set the Amplitude value to 0.07 (the fraction of amplitude contrast in the CTF; 0.07-0.10recommended for cryo-electron microscopy (EM) data).
Set the Defocus range as 1.0-4.0mm based on the acquisition defocus range, with some margin.
To start with, set the spatial frequency Range to 25.0-5.0A ˚(0.14-0.68Nyquist (Ny), unbinned pixel size of 1.69 A ˚) to test the CTF fitting.
Note: Adjust the spatial frequency range to include more of the lower or higher frequencies based on the quality of the CTF fitting test with a few frame movies before applying the fitting to all movies (switch to the Fourier Space tab and click Process Only This Item's CTF to see whether the fitting range represented by the gray rectangle is optimal to the signal in your data).A range of 22.0-5.0A ˚(0.15-0.68Ny) worked well for the example data.
iv.Under the Motion part of the Preprocessing section, set the spatial frequency range for the motion estimation to 67.8-13.6A ˚(0.05-0.25 Ny) and the B-factor to À500 A ˚2. v.Under the Models section, do the following.
Set the Defocus model resolution as 2 3 2 3 1, with the first two defining the spatial resolution and the third as temporal resolution for the defocus estimation.

Note:
The estimation is done on the sum of frames due to insufficient signal in a single frame.A value of 2 at the first two parameters means no local defocus estimation performed.The recommended settings in the Warp manual 5 3 5 3 1 may also work depending on the unbinned pixel size of your data.
Set the Motion resolution to 5 3 5 3 8 for this dataset, with the first two values defining the spatial resolution and the third defining the temporal resolution (8 frames per movie in the example dataset).
Note: As the manual suggests, set a temporal resolution value that matches the overall electron dose per pixel if a low dose rate (<1 e/px/frame) is used.The first two spatial resolution parameters can also be set to 1, if the signal in a single tilt movie average is suspected to be too low for alignment.
vi. Skip the Pick Particles option in this case.vii.Leave the Output section as default.Do not skip any frames in this case.The aligned movie Average will be written out.e. Start processing and check the results.
i.With the parameters set in the previous steps, click the START PROCESSING button to start.The button will be switched to STOP PROCESSING once the procedure starts.
Note: Warp provides an overview tab to show the processing status of all movies and plots for the defocus value, the estimated resolution, and the average motion per frame in the first 1/3 of the processed movies.On the left side of each plot, a histogram provides additional evaluation of the data quality (Figure 2).
ii. Click to STOP PROCESSING if parameters need be changed or the processing is completed.
Note: In this case, we only used one GPU, but Warp will try to use all compatible GPUs available on the system.Therefore, deselect the unwanted GPUs from the upper right corner of the window bar.
f.When processing is done, check the processing results of individual tilt movies.i. Collapse the settings panel on the left by clicking on the arrowhead between the settings and the Overview/Fourier Space/Real Space panel.ii.Switch to the Fourier & Real Space tab, and inspect the results by clicking through the navigation bar (green/blue color in most cases) at the bottom of this tab (with the left and right arrow keys on the keyboard).iii.For fine-tuning the CTF fitting parameters for individual tilt movies, use the PROCESS ONLY THIS ITEM'S CTF button after adjusting the spatial frequency range under the CTF section (switch back the settings panel).
Note: If this is done, the previously processed items with different settings will change their processing status to Outdated in yellow.Changing the spatial frequency range back to the previous values after individual tilt movies are fine-tuned should change the process status of the majority of movies back to green for proceeding to the next step.One can set the filter thresholds in each plot in the Overview tab so that movies with fitted parameters out of the selected ranges can be filtered out when proceeding with the Warp pipeline.Alternatively, one can manually exclude individual tilt movies by clicking on the check box in front of the name of each tilt movie at the navigation bar in the Fourier & Real Space window (''-'' means automatic filters).In this example (Figure 2), movies were not excluded at this stage.
2. Export tilt-series stacks from the summed tilt frame movies generated in the last step.a. Switch Warp to tilt-series mode by choosing the TomoSTAR input format at the top of the window.b.In this mode, click on the Import tilt series from IMOD button at the Overview tab and a dialog pops up for choosing the path to the folder containing the mdoc files written by SerialEM and the path to the original input tilt movies (''frames'' folder).c.Leave the pixel size as 1.69 A ˚and dose per tilt as 0 e/A ˚2.Warp reads the dose value from the mdoc files.d.Uncheck the Invert tilt angles in the Warp version 1.0.7 beta (option changed to invert tilt angles by default in the Warp version 1.0.9).e. Click on CREATE STACKS FOR IMOD.
Note: Warp will write a new directory named ''imod'' containing separate folders for each tiltseries stack named according to the corresponding mdoc name.

Timing: 7 h
This section describes the alignment of tilt series generated by Warp with Etomo in the IMOD package.Processing of the tilt series until the fine alignment step (included) is sufficient to allow Warp to import the alignment parameters from IMOD.Continue with refining the CTF estimation for tilt series and reconstruct tomograms in Warp to the desired binning size for filament tracing in the next section.
3. Perform tilt-series alignment with the Etomo module (IMOD version 4.9.4 described here) in the imod subfolders containing the individual tilt series written by Warp (Figure 3A).Leave parameters as default unless specified otherwise in the following steps.
a.With a Windows command prompt or Linux terminal, navigate to the subfolder for the first tilt series, and type ''etomo'' to launch the software.i. Select Build Tomogram to enter the setup interface for single tomogram reconstruction.
ii. Specify the tilt series to be reconstructed in Dataset name.
iii.Select cryoSample.adoc in Templates and set Axis Type as Single axis while keeping Frame Type as Single frame.iv.Click on Scan Header to extract the Pixel size and Image rotation from the input data.v. Set Fiducial diameter as ''0'' as no fiducial markers were used for tracking with this dataset.(B) A screenshot from the Warp GUI showing the refined CTF processing result of the full tilt series in A (the screenshot shows details for the 0-degree tilt) after importing alignment parameters determined in IMOD.The CTF fitting quality is normally expected to be worse at higher tilt angles compared to that at lower angles.(C) A tomographic slice of the data shown in (A) following Warp reconstruction and deconvolution.The tomogram was deconvolved with the default parameters during the Warp reconstruction procedure for better visual inspection and manual filament tracing in the next step.The filament is curved, but its relatively straight segments can be used for subtomogram averaging.
vi. Select the ''Extract tilt angles from data'' option under the Axis A section. vii.Click on the View Raw Image Stack button to inspect whether some views of the tilt series need be excluded at this point due to bad quality, e.g., views of areas largely covered by ice.
CRITICAL: If views are excluded during the Etomo processing, edit the mdoc file accordingly to remove those views to avoid mismatch with the subsequent Warp processing that makes use of the alignment parameters from Etomo.Note: This should produce a reconstruction (of the tomogram and map) with correct handedness given the microscope settings in the example dataset.
iii.Click on ''Done'' to proceed.j.Skip the Clean Up step for now and click on ''Done'' to finish the reconstruction of the tilt series.Intermediate files of large sizes can be cleaned altogether when necessary.
CRITICAL: Complete the reconstruction of one tomogram per batch of data for determining optimal parameters for tomogram reconstruction (e.g.Z dimension size), assessing the data quality, and for checking the consistency between IMOD and Warp results.
Optional: Reconstructing tomograms in IMOD batch mode saves time especially with a larger dataset (https://bio3d.colorado.edu/imod/doc/batchGuide.html).k.Complete the reconstruction of one tomogram as detailed above and save the parameters in a ''batch directive file'' by using the Export option in the Etomo interface.l.In the batch reconstruction interface, set it as the ''Starting directive file'' and select ''cryoSample.adoc'' in the System template box.m.Import all the remaining tilt series in the ''Stacks'' tab, check parameters in the ''Dataset Values'' tab and set it to ''Stop after Fine alignment''.Press ''Run'' to start.Assess the alignment results and adjust for individual tilt series if needed.
Optional: One can use AreTomo 15 (https://github.com/czimaginginstitute/AreTomo2)for automatic batch alignment and reconstruction of tomograms with GPU environment available.Warp also accepts alignment parameters/files generated by AreTomo.i. Specify the folders for mdocs, original frame movies, and IMOD processing results (select the subfolder named ''imod'' created by Warp).
CRITICAL: If there are views excluded during the Etomo alignment, edit the mdoc file accordingly to remove those views for Warp to consistently take the corresponding alignment parameters from Etomo.
CRITICAL: Due to a difference in handling tilt angle direction in Warp compared to IMOD, there is an option to invert tilt angle when importing tilt series from IMOD.When the example data were processed with the Warp version 1.0.7 beta, the tilt angle convention was set inversely to that in IMOD.Thus, additional flipping of subtomogram averages was necessary to produce the correct map handedness.
Note: If all IMOD processing results are found, checkboxes in the ''Aligned'' column will automatically be ticked for all tilt series.Warp requires the xf and taSolution.logfiles in each of the ''imod'' reconstruction subfolders.
CRITICAL: According to the convention, the tilt axis in the raw movie frames is approximately aligned to the image X axis, while that in the tomogram is aligned to the Y axis.Thus, taking for example data recorded on a K3 detector, the unbinned image dimensions are 6000 3 4000 pixels and the tomogram dimensions should be 4000 3 6000 instead.The Z dimension with this example dataset was set to 1000 based on the sample thickness, leaving some margin at the border of tomograms to avoid problems later during subtomogram extraction.
c. Leave all preprocessing parameters/settings as those for processing of raw movie frames.
i. Go to the Fourier Space tab and click on ''PROCESS ONLY THIS ITEM'S CTF.''ii.Click on ''CHECK TILT HANDEDNESS'' to check if the defocus handedness is correct.
CRITICAL: This relates to the defocus convention.Improper handling will limit the maximum attainable resolution of the subtomogram averages.If the correlation value at this step is close to 1, it indicates correct defocus handedness, as in the case of the example data.If the value is close to À1, follow instructions to flip the defocus handedness.
iii.Click on START PROCESSING to process the CTF for all tilt series in batch (Figure 3B).d.When the previous step is done, go to the Overview tab and click on the RECONSTRUCT FULL TOMOGRAMS button.i.A new interface pops up that allows reconstruction of tomograms with user-defined parameters.Set the pixel size based on the binning factor required for the next step of data processing.Here, 6.78 A ˚is used to obtain 43 binned tomograms.ii.Choose ''Normalize input images'' which is useful when data are processed in RELION 11 in the next steps.iii.Use the ''Also produce deconvolved version'' option with the default parameters, which significantly improves the reconstruction contrast in the example data (Figure 3C).
Note: If denoising is needed to obtain even better contrast, use the ''Reconstruct half-maps for denoising'' option.One can further denoise the tomograms either with Warp or other software such as cryoCARE. 16v.For this data, use the ''Include items outside of filter ranges''.v. Click on RECONSTRUCT to start.The output tomograms will be written to an automatically generated subfolder named ''reconstruction'' or ''reconstruction/deconv'' (for the deconvolved tomograms).
Note: Reconstruction of full tomograms at lower binning can result in slow processing, or even crashing of Warp.Also, the lower contrast in lower binning tomograms will not be beneficial for later steps of filament tracing.
Optional: Tomograms reconstructed in IMOD or AreTomo can also be used for filament tracing.But pay attention to possible pixel shifts or image rotation/flipping in comparison to the Warp reconstruction that will ultimately be used to generate subtomogram in the later steps.

Filament tracing and subtomogram generation
Timing: 6-9 h This section demonstrates how to use the filament model function in Dynamo 8 to manually trace the backbone of the isolated filaments in the 43 binned tomograms reconstructed in Warp, to generate positions for subtomograms based on the geometry of the filaments, and to crop subtomograms for next steps of alignment and averaging.Call the dtmslice interface from the dpreview GUI and manually mark the two ends (''anchors'') of each filament with the [1] and [2] keyboard keys.
CRITICAL: Avoid including heavily curved regions at the tracing step already.
Create orthogonal sections along the line connecting each pair of two anchor points and click on the centers of these sections to trace the ''backbones'' of filaments (backbones are smoothed in later steps).

Note:
For the example data, only modify the sidelength (the box size) for generating sections of 80 pixels (a bit over twice larger than the filament diameter; pixel size: 6.78 A ˚), a value large enough to fully cover the cross-section view of sometimes curved filaments.
Note: If needed, modify the step parameters in the Traverse path box (Sections along path in later Dynamo versions) in the GUI to adjust how many sections to generate along the traverse path (the default 1:10:100 means to divide the distance between two anchor points by 100 parts and create a section ever 10 parts).
In the slices view, use the [C] keyboard key to choose the center of the cross-section views on different slices.v. Convert these manually clicked points into coordinates for subtomogram/particle extraction: choose the filamentWithTorsion model (Figure 4A) to generate equidistant (dz: 8 pixels = 54.24A ˚) segments of filaments with ''artificial'' torsion angle (dpsi: 36 degrees), in order to reduce the dominance of the missing wedge during alignment later.
Note: There are four filament model types available in Dynamo which can be selected to fit the type of filaments being processed: filamentWithTorsion, filamentSubunitsOnHelix, fila-mentSubunitsOnRings, or filament (with randomly distributed positions in the filament tube).
vi. Save the models with the positions and angles for cropping subtomograms.
Optional: By incorporating loop scripting, it is also possible to input into Dynamo multiple series of points (along the traced filaments) as a matrix of their x/y/z coordinates created elsewhere (e.g.Amira) and generate geometrically computed sampling points for subtomogram extraction with MATLAB code provided in the Dynamo wiki (https://wiki.dynamo.biozentrum.unibas.ch/w/index.php/Filament_types_code_example).Optional: Subtomograms can also be reconstructed in Warp.Use the dynamo2warp function in the dynamo2m package 14 to convert a Dynamo table to a STAR file for Warp reconstruction (detailed later at step 10).In our initial pipeline, we converted a Dynamo table to a motl file (for TOM/AV3 9,10 alignment) and/or a STAR file (for Warp/RELION) with MATLAB scripts in order to test alignment with different software at different steps.

Initial subtomogram alignment and averaging
Timing: 2-3 days While later high-resolution processing is performed in RELION, we found it beneficial to perform initial alignments, especially for non-globular structures, in subtomogram averaging-dedicated software, such as Dynamo.This section describes the strategy to align the 43 binned and 23 binned subtomograms (6.78A ˚/pix and 3.39 A ˚/pix, respectively) with Dynamo for easy plots of aligned positions/angles and for ensuring uniform directionality of subtomograms positioned along the same filament, which is particularly important for filaments with polarity.
Note: For detailed explanation of the parameters used in Dynamo alignment, please refer to the Dynamo wiki mentioned above.We set up working directories for the following Dynamo alignment projects at the same level so that the tables, particles, and templates can be easily accessed by these projects at a working directory one level upper.i. Open the masks interface.Use the Mask editor to create a file, e.g., mask64_r17_h25_ g2.em, with the ''raw_template.em''as a reference.
Note: This corresponds to a cylinder mask with a box sidelength of 64 pixels, radius of 17 pixels, height of 25 pixels and Gaussian smoothened by a factor of 2; it should fit the template size in the overlaid view.Use this mask for the alignment, classification as well as calculating the Fourier Shell Correlation (FSC).The classification and FSC masks are not used here, but must be set for the project to run.
Set the Fourier mask on template as ''fill with ones'' by clicking on that button at the ''Input masks for project'' interface.ii.Set the alignment numerical parameters (Figure 4B).Key parameters are for example: iterations: 4; cone aperture and sampling: 45 and 15 degrees; azymuth rotation range and sampling: 45 and 5 degrees; refine and refine factor: 2 and 2; high and low pass: 1 and 12 Fourier pixels which correspond to a maximum achievable resolution of 36 A ˚at the first iteration of alignment (64 (box_size) / 12 (low_pass) 3 6.78 (pixel_size) = 36 A ˚); shift limits: [4, 4, 4] in pixels; shift limiting way: 1. iii.Set the computing environment depending your local settings for running Dynamo.
Either the Standalone option with a few CPU cores or GPU standalone with the GPU identifiers properly set worked in our case.iv.Click on the check and then the unfold button to finish preparation of the project.v. Exit the GUI and submit the job either by running ./bin4_align_1.exe in the Dynamo prompt, or submit the job to a cluster following the detailed instructions on the Dynamo wiki.
Note: Dynamo alignment parameters can also be set simply by command lines without the GUI.
Note: One can also use the average from a single long filament as an initial template/reference for alignment.As we have tried to keep uniform directionality during the filament tracing step, the average of all subtomograms gives better signal-to-noise ratio and should not be dominated by subtomograms from filaments with opposite directionality.
Note: One can also try using a large cone for angular search (e.g.360/45).With the example data, such test resulted in a worse average showing fake 2-fold symmetry, likely due to averaging of filaments with mixed polarities.Thus, for later steps of alignment at lower binning, even smaller angular and shift limits than those in the first alignment stage are recommended.
Note: Use dynamo_table_plot function to plot orientation and positions of subtomograms after alignment.Dynamo has multiple flags regarding how the plots are displayed, e.g.colored according to cross-correlation (cc) values.The values in tables can be plotted in histograms, e.g. the shifts in the refined tables, which helps for evaluation and optimization of the refinement procedures beyond visualization of the resulting averages.b.Launch the project GUI with the dcp command and set the same masks as for previous alignment projects.
Note: Here, the FSC mask will be meaningfully applied to each of the averages of the half dataset at each iteration for estimating resolution and defining the lowpass filter parameter for the next iteration.
c. Derive an ABP project from this single reference project: i.In the GUI, choose the following options in the Multireference tab > Adaptive filtering (''golden standard'') > Derive a project.ii.In the newly launched interface, rename the derived ABP project as ''bin4_align_2_eo'' following the convention and click the Enter key.d.Generate the odd/even references by averaging the odd/even subtomograms in the eo project GUI (at the template interface).e.At the template interface, use the visualize FSC button to estimate the initial frequency given in Fourier pixels.
Note: The X value corresponding to a Y value of around 0.143 in the FSC plot when moving the cursor along the curve 17 is the initial frequency.This will be used for setting the lowpass filter parameter of the first round of alignment.
f. Set the initial frequency value together with an FSC threshold of 0.143 and a resolution pushback of 2 Fourier pixels in the dcp GUI under the Multireference tab > Adaptive filtering (''golden standard'') > Edit parameters for Adaptive Filtering run.g.Set the numerical parameters and computing environment as for previous Dynamo projects.h.Run the project.i.When finished, launch the project GUI again and use the Plot attained resolution FSC for all iterations option under the Adaptive filtering (''golden standard'') option to check the evolution of FSC plots and resolutions in Fourier pixels in the different iterations.10.Combine the tables from the bin4_align_2_eo project and prepare for subtomogram reconstruction and alignment at 23 binning (pixel size: 3.39 A ˚).
Note: Due to the difficulty and unnecessity in reconstructing full tomograms at 23 binning for Dynamo reconstruction, we choose Warp for reconstructing subtomograms at lower binning.
a.As mentioned in the optional section of step 6, use the dynamo2m package (dynamo2warp function) to convert the Dynamo table to a Warp STAR file.b.In the Warp GUI, use the TomoSTAR mode, and click on the RECONSTRUCT SUB-TOMO-GRAMS button to create subtomograms and their corresponding 3D CTF models.
Note: CTF models and the output STAR file are not used at 23 binning, but those reconstructed at the unbinned pixel size will be important for the next step processing in RELION.
i.In the pop-up options for exporting subtomograms, set the ''Coordinates use 6.78A ˚/px'' if the coordinates are at 43 binning (pixel size: 6.78 A ˚) in the STAR file that is converted from a Dynamo table.
Note: In our case without the dynamo2m package, we already scaled the coordinates to 23 binning with a MATLAB script while converting them from a dynamo table to a Warp STAR file.Thus, set it to ''Coordinates use 3.39 A ˚/px''.
ii. Set the ''Output will be scaled to 3.39 A ˚/px'' for 23 binned subtomograms.
Note: Multiple methods such as the tom_mrcread and tom_emwrite functions in the TOM package 9 within MATLAB, the mrc2em module in PyTom, 18,19 or the e2proc3d.pyin EMAN2 20 can be used to perform this conversion.
11. Align the 23 binned subtomograms and prepare for alignment of unbinned data.a. Create a new Dynamo ABP project, following the same procedures at step 9. i. Generate a new mask twice the size of that used for 43 binning.
ii. Decide on the initial lowpass frequency by calculating from the new odd/even averages at 23 binning.iii.Use smaller angular and shift search ranges.
Note: When the ABP project at 23 binning (pixel size: 3.39 A ˚) converges, i.e. reaches a resolution of 13 A ˚in this case (Figure 4C, right), continue to prepare for alignment of unbinned subtomograms (pixel size: 1.69 A ˚).
Note: Originally, we aligned the 23 binned subtomograms in TOM/AV3 instead of Dynamo for speeding up the alignment in parallelized jobs essentially on the cluster (not working efficiently in Dynamo versions installed on our cluster with the corresponding setup).b.Clean up clashing subtomograms (particles that have shifted to occupy the same position in the helical filament) based on their distances to each other and their cross-correlation (cc) values within MATLAB. 21te: Here, a low threshold (10 pixels at 23 binning, i.e. 3.39 nm) was used after checking the histogram of distances of subtomograms to their 20 nearest neighbors.For clashing subtomograms, the ones with higher cc values were retained.1209 subtomograms were kept for the next step processing.
c. Convert the cleaned Dynamo table to a STAR file for Warp and reconstruct the unbinned subtomograms in Warp following the same procedures at step 10. d.Perform one round of Dynamo alignment to make sure that the cleaned table/STAR file works before proceeding to RELION in the next steps.

Timing: 11 days
This section describes settings for aligning the unbinned subtomograms (pixel size: 1.69 A ˚) with refined positions and orientations obtained from the steps above in RELION, including 3D auto-refinement, helical search, 3D auto-refinement with helical reconstruction, and further 3D classification to distinguish different conformational states of the filaments (Figure 5; RELION version: 3.1.0).
Note: We recommend referring to the guidelines for RELION 3D auto-refine (https://www3.mrc-lmb.cam.ac.uk/relion/index.php/Refine_a_structure_to_high-resolution).b.Convert the average from the last round of Dynamo alignments from a .emfile to a .mrcfile in Chimera. 12.Use the Volume Viewer/Tools/Volume Filter to scale the volume by À1.
ii.As the Dynamo average likely does not contain pixel size information, set the correct pixel size (1.69A ˚) with Chimera under Volume Viewer/Features/Coordinates.iii.Save the map as a new map named ref_unbin.mrc(Figure 5A).c.Generate a cylinder mask with RELION.Use the command as following: CRITICAL: Check the RELION instructions for detailed explanation about the mask parameters.In brief, measure the filament outer diameter based on ref_unbin.mrc.The combination of boxdim (in pixels), cyl_outer_diameter (in A ˚), sphere_percentage gives a central Z length of about 30%, which is optimal for RELION helical reconstruction in later steps.Width (in pixels) refers to the value for creating a soft-edged mask.d.Set up the RELION job in a GUI.Leave the setup parameters as default unless specified otherwise.i. Specify the newly converted STAR file, the ref_unbin.mrc,and the cylinder mask file in the I/O tab.ii.In the Reference tab, leave the ''Ref.map is on absolute grayscale?''as No because the map was not generated in RELION and thus may have a different absolute intensity grayscale from the RELION requirement.iii.Set the Initial low-pass filter as 30 A ˚.In the Optimisation tab, set the Mask diameter as 300 A ˚and the ''Use solvent-flattened FSC?'' as Yes.iv.In the Auto-sampling tab, set the Initial angular sampling as 3.7 degrees and the Local searches from auto-sampling as 3.7 degrees.v.For the Compute and Running tab, set parameters according to the computing environment.
Note: In our case, we copied the particles and their corresponding 3D CTF models to a directory on the cluster and set the Number of MPI procs, Number of threads, GPU count, Memory, Walltime and Minimum dedicated cores per node as 3, 7, 2, 120 GB, 3-23:00:00, 15, respectively.
vi. Click on Run to submit the RELION job to a cluster.It took 33 iterations to converge in this case.e.In RELION Post-processing, provide One of the 2 unfiltered half-maps and the refinement mask as the Solvent mask, and a B-factor (e.g., À500 A ˚2).When the job is done, check the estimated final resolution in the output window, as well as in the output files.
Note: The resulting resolution was 10 A ˚with the refinement mask applied, showing clear separation of subunits along the helical turns (Figure 5B).
13. Perform helical parameter search in RELION.a.With the output average from the Post-processing job in step 12, estimate the helical rise and twist parameters by measuring the helical pitch and counting the number of subunits per turn.
Note: Rise equals the helical pitch in A ˚divided by the number of subunits per turn.Twist equals 360 degrees divided by the number of subunits per turn, with a positive sign for a right-handed helix, which is inverse to the actual handedness as explained above.
b. Set the ranges in the following command line: Note: The output from this command is: rise 4.46 A ˚; twist 27.08 degrees.
The settings are similar to those in step 12, except for the following.a. Use the output STAR file from the 3D auto-refine job in step 12. b.Use the output average from the Post-processing job in step 12 as reference map.c.Set up the RELION job in a GUI: i. Specify the STAR file, the reference map, and the reference mask file (the same as in step 12) in the I/O tab.ii.In the Reference tab, leave the ''Ref.map is on absolute grayscale?''as Yes.Rise search -Min, Max, Step (A ˚): 3.8, 4.6, 0.05.iv.In the Running tab, increase the Walltime as this job took longer than the refinement job without helical reconstruction with the RELION version used.v. Click on Run to submit the RELION job to a cluster.d.In RELION Post-processing, use one of the two unfiltered half-maps of the previous step and the refinement mask as the Solvent mask, and perform B-factor estimation automatically.

Note:
The resulting resolution was 7.3 A ˚with an ''Auto B-factor'' of À382 A ˚2. Re-doing helical search gives a helical parameter set of 27.16 degrees and 4.17 A ˚(Figure 5C).5D, with the corresponding FSC curves in Figure 5E).
CRITICAL: EM maps at a resolution of 8 A ˚or higher should exhibit some features of secondary structures such as alpha helices, which should be critically assessed along the alignment/refinement steps, rather than considering only the reported resolution.

Multi-particle refinement and map evaluation
Timing: 3 days Based on the refinement results above, this section shows how to perform multi-particle refinement in M (following helical symmetry expansion with RELION, as M versions until 1.0.9 do not support helical symmetry operation) and rerun RELION 3D auto-refine with the refinement results from M. At the end, generate FSC plots and examine local resolution of maps.17.Prepare files for M refinement.a. Make a copy of the original Warp preprocessing, especially the .xmland .tomostarfiles as a backup.M refinement will overwrite some of the files.b.Copy the 2 half maps and particle STAR file from the RELION 3D auto-refine job to a folder designated for M processing (ideally as a subfolder in the directory that contains the Warp frames folder): it0xx_half1_class001_unfil.mrc,it0xx_half2_class001_unfil.mrc, and run_ it0xx_data.star(renamed to run_it0xx_data_ori.star)c.For an M version that does not support helical symmetry operation, perform helical symmetry expansion with the RELION command: Note: This generates a new STAR file with an expanded particle list according to the symmetry (13-fold larger).Downgrade the RELION STAR file with the relion_star_downgrade command in the dynamo2m toolbox and rename the STAR file to jobxxx_run_it0xx_data.star for M refinement (M version 1.0.9 recognizes the suffix of the file as _data.star).
>relion_particle_symmetry_expand --i run_it0xx_data_ori.star--o run_it0xx_data_expanded.star --helix true --twist 27.17 i. Link the new population to the data source by clicking ''Manage data sources'' and then ''ADD LOCAL'' at the interface.ii.Choose and import the ''previous.settings''file from the previous Warp processing folder.iii.Set a name for the data source.Choose to ''Include items outside of filter ranges'' to include all data selected.iv.Continue to click ''Create'' and close the pop-up interface when done.A .source file will be created within the frames folder by default.d.Click the ''+'' icon to add species, for example the Class 3 average obtained in step 16.  i. Use the ''From scratch'' option for the first M refinement job and click on ''ADD''.
ii. Set a name for the species and use a parameter set of 220 A ˚diameter (filament diameter), 2600 kDa (the estimated molecular weight of the region included in the refinement mask; estimated based on 39 protein subunits plus 6 3 39 = 234 nucleotides for RNA, with a small margin), C1 symmetry, 1 temporal sample for poses.iii.Select the half-maps from the M processing folder above for the Class 3 average for example and input the correct pixel size.Use the ''filter both to A ˚'' option to visualize the input maps (filtered to a resolution lower than the estimated value, for inspection purpose).iv.Set the mask file with the cylinder mask in the M processing folder.v. Specify the symmetry expanded and downgraded STAR file.Input the pixel size for coordinates and shifts in the STAR file which relates to the binning factor at which alignments are performed.vi.Select the source file under ''Use these sources for name matching''.M then displays the correct particle number read from the STAR file that can be matched to this data source file.i. Open a pervious RELION average (read as #0) and the M average (read as #1) in the same Chimera window.ii.Superpose the two averages by the ''fit in map'' option.iii.Use the following command to rescale the box size and set the origin for the M average: iv.Save the resampled average in .mrcformat and use it as an input reference in a new RELION 3D auto-refine job.d.Run a new RELION 3D auto-refine job with helical reconstruction, with settings similar to step 16. i. Use a smaller auto-sampling angle as the particle orientations have been refined extensively in previous steps.ii.Do post-processing for this new average to obtain a final refined map.iii.Search for helical parameters again with the post-processed average.

Note:
The helical parameters remained almost the same after M refinement compared to those before, but the calculated resolution with the 0.143 criterion and map quality improved substantially with M refinement (Figure 6).
Note: M version 1.1 beta could support helical refinement but needs be tested.According to the software developer, one has to modify the ''Symmetry'' Value (e.g., Hel| 27.17|4.21) in the .speciesfile as setting the helical parameters is not yet supported directly in the GUI.

CRITICAL:
In case M refinement does not improve the resolution of maps, but rather deteriorates it, make a copy of all files from an earlier version of that species (contained in [species folder]/versions, sort by date to get the version needed) except for the [species name]_particles.starfile, to get back to the maps with higher resolution and try other combinations of refinement options.
Note: For non-helical objects, one can use the export sub-tomograms function in M instead of resorting back to Warp with a converted STAR file.
Note: For M refinement of particles of a small number (e.g.Class 4 from step 16), set up a refinement job with two species using STAR files and half maps for both species (Classes 3 and 4 together).Refinement worked when including only the image warp grid, particle poses, stage angles, and volume warp grid options.We continued to reconstruct subtomograms after M refinement and run a new RELION 3D auto-refine job for the results described here.

Map quality evaluation and model building.
a. Run RELION Post-processing job to obtain FSC plots based on the unfiltered half maps, with or without a given mask.
Note: The resolutions we reported as 4.5 A ˚for the majority class average and 6.3 A ˚for the minority class average were derived with the cylinder refinement mask applied.The maps after M-RELION refinement show higher quality in that secondary structures can be easily visualized in the final maps.Note: As mentioned above, we generated the maps with inverted handedness using Warp 1.0.7 beta.Run the command line below to obtain maps with correct handedness: d.For building a model, as detailed in Zhang et al., 1 i.Generate an initial model using homology with I-TASSER 22 or using AlphaFold. 23i.Use the generated model as a starting point for iterative automated and manual refinement in Phenix 24 and Coot. 25ii.Generate a segmented map for the region corresponding to the tetramer model with the volume zone command in ChimeraX.iv.Validate the real-space per-residue cross-correlation of the tetramer model to the map generated in Phenix.
Note: A tetramer model was built in order to better analyze the interaction interface among monomers of the same turn as well as that between the upper and lower turns.

EXPECTED OUTCOMES
Based on 20 cryo-tomograms, which contained about 20 viral nucleocapsids, we extracted 1297 subtomograms from the relatively straight nucleocapsid segments and successfully obtained 2 reconstructions at 4.5 A ˚and 6.3 A ˚.The 4.5 A ˚map was of sufficient quality to serve as the basis for the building of an atomic model of the mumps viral nucleocapsid protein (N terminal 3-405 amino acids) with its RNA hexamer as one subunit for the assembly of the helical filamentous structure.We then used this model to interpret the 6.3A ˚lower resolution minority class reconstruction by careful visual inspection in Chimera/ChimeraX, and provided information on the differences in helical pitch and assembly compactness between the two maps.Comparing the two models in detail revealed differences in the interaction interfaces between monomers leading to the different assemblies, as well as their conformations that indicate differences in the surface accessibility of the bound RNA (genome) and C-terminal regions (Figure 7).
Beyond the example dataset of extracted nucleocapsids described in this protocol, we have also processed cellular cryo-ET data of relatively straight nucleocapsids using steps similar to those discussed here, with the following adjustments: 1) the tracing of the helical filaments was performed in Amira using dedicated functions 1 ; 2) the generation of equidistant sampling points along the helical segments was done with a MATLAB script 26 ; 3) initial alignment of the subtomograms required more strict control of the Euler angles to avoid change of directionality of subtomograms along their respective filaments; 4) checking the directionality of each filament was complicated by the lower signal-to-noise ratio of the cellular cryo-ET data compared to that of the isolated filaments.Thus, nucleocapsids with unclear directionality based on the inspection of the per-filament average were largely discarded at the initial alignment steps for achieving high resolution maps.Nevertheless, a consensus map of 6.5 A ˚was finally obtained from 5 tomograms, 68 helical filaments and 2178 subtomograms.

LIMITATIONS
Transfer of the workflow described in this protocol to the processing of different filament types and morphologies is expected to be successful, provided that the filaments are sufficiently straight or >relion_image_handler --i xxx.mrc --o xxx_flipY.mrc--flipY contain straight regions.Depending on the filament types and available software in your local settings, data processing workflows that integrate other software, such as those described for the processing of LRRK2-microtubule filaments, 27 the COPII coat reconstituted on lipid membranes, 2,28 the Ebola virus nucleocapsid, 29 or the skeletal thin filaments 30 can also be tested.

TROUBLESHOOTING Problem 1
Uncertainty about the options for correcting gain reference during Warp preprocessing (related to step 1).

Potential solution
The correct gain options need to be tested according to the camera setting and serialEM data saving options.Warp indicates whether the ''Transpose'' option is needed automatically.According to the ''r/f'' values of the raw tif images (https://bio3d.colorado.edu/SerialEM/hlp/html/about_camera.htm#norm_frames),different combinations of ''Flip X axis'' and ''Flip axis'' are needed.
Using images with good contrast, test the flip combinations by processing a few movies without motion correction until a visible gain pattern disappears in the average images.Or, generate a sum of dozens of averages without motion correction.Check the gain pattern in this summed image and compare it to the gain reference image to decide on the flip options.

Problem 2
Inconsistency between Warp and IMOD reconstruction (related to step 4).

Figure 1 .
Figure 1.Overview of the computational pipeline for subtomogram averaging of filamentous helical assemblies described in this protocol Parts of figures in Zhang et al. 1 published under a CC-BY license are reused.

Figure 3 .
Figure 3. Tilt series CTF estimation and tomogram reconstruction in Warp (A) A tilt image from one of the tilt series in the example data.(B)A screenshot from the Warp GUI showing the refined CTF processing result of the full tilt series in A (the screenshot shows details for the 0-degree tilt) after importing alignment parameters determined in IMOD.The CTF fitting quality is normally expected to be worse at higher tilt angles compared to that at lower angles.(C) A tomographic slice of the data shown in (A) following Warp reconstruction and deconvolution.The tomogram was deconvolved with the default parameters during the Warp reconstruction procedure for better visual inspection and manual filament tracing in the next step.The filament is curved, but its relatively straight segments can be used for subtomogram averaging.

4 .
Refine CTF estimation for tilt series and reconstruct tomograms in Warp.a. Re-open Warp and click the IMPORT TILT SERIES FROM IMOD button to import the alignment results from IMOD in the TomoSTAR mode.

Figure 4 .
Figure 4. Filament model generation and initial subtomogram alignment in Dynamo (A) An example of ''filament model with torsion'' generated in Dynamo.Lines with short perpendicular lines at their ends indicate where and with what orientations subtomograms are generated (positions and angles are recorded in a Dynamo table).(B) An illustration of the multilevel refinement of subtomogram orientations generated in Dynamo (top: dark blue, light blue, and yellow are colored according to the order of sampling grid in three consecutive levels).The corresponding numerical parameters used in the first Dynamo alignment project are shown at the bottom.(C) Subtomogram averages after alignment at 43 binning and 23 binning of 1297 particles from all filaments in the example data.Y is flipped to the correct handedness.Pixel sizes are indicated.Parts of figures in Zhang et al. 1 published under a CC-BY license are reused.
a. Create an initial average of all subtomograms generated from the previous step as a template for Dynamo subtomogram alignment.With Dynamo loaded in a terminal, run the following in the working directory.b.Set up a Dynamo subtomogram alignment project with the following command: c.Modify the alignment parameters including the masks in the GUI and run the Dynamo project.
a.The Warp-generated STAR file from the last step is in a format compatible with earlier RELION versions.For RELION version 3.1 or later, use the relion_convert_star command (with the RELION module loaded) to convert an old STAR file format to a new one whenever needed.Troubleshooting 3.CRITICAL: New-style RELION STAR files (version 3.1 onwards) mainly differ from old formats in: 1) the new-style includes the optics group part; 2) the new-style uses OriginX/Y/ZAngst columns to replace the OriginX/Y/Z and the units change from pixels to angstrom.Thus, these values in the new STAR files equal values in Ori-ginX/Y/Z 3 pixelSize in angstrom.We used a MATLAB script for the conversion to old STAR for Warp.The dynamo2m package enables this with python scripting (relion_-star_downgrade command).

Figure 5 .
Figure 5. Subtomogram averages imported into RELION for refinement and helical reconstruction Pixel size is 1.69A ˚(unbinned data) for all averages.(A and B) Half map, Gaussian filtered, without multiplication by the alignment mask (shown next to each map).(C and D) Half map, filtered to the determined resolutions in RELION Post-processing jobs, with the alignment mask (next to the map) multiplied.The maps in (D) are results for each class, obtained by M refinement after classification.All maps are Y flipped to the correct handedness.(E) Masked FSC curves for the maps in (C) and (D), using the same alignment mask.The dotted line indicates the 0.143 threshold.Parts of figures in Zhang et al. 1 published under a CC-BY license are reused.

Figure 6 .
Figure 6.Comparison of the subtomogram averages before and after M refinement Shown are the subtomogram averages before (top) and after (bottom) M refinement, from all the particles (A) or those belonging to the majority class (B), along with their masked FSC curves (C).The alignment mask shown in Figure 5D was used for refinement and post-processing of all averages shown here.The dotted line in (C) indicates the 0.143 criterion.bM, before M refinement; aM, after M refinement.Numbers refer to the determined map resolution.All maps are Y flipped to the correct handedness.Parts of figures in Zhang et al. 1 published under a CC-BY license are reused.

>vop resample # 1
onGrid #0 b.Run a RELION Local resolution job to calculate the local resolution and render the map accordingly in Chimera/ChimeraX. 13c.Prepare for building a structural model based on the final map and check the model-to-map cross-correlation.

Figure 7 .
Figure 7.Comparison of the subtomogram averages of two classes of the isolated nucleocapsids and that of the incell ones The majority (green) and minority (orange) class averages of the isolated nucleocapsids and the average of the in-cell nucleocapsids (purple) are compared from the outer surface view (A) and lumen view (B).RNA is depicted in yellow in all images.The white arrow in (A) (middle) indicates movement of subunits in the upper turn towards the lower turn in the minority class average, in comparison to the other two averages.The white circles in (B) denote a region in the density which is not seen in the minority class average possibly due to flexibility, in contrast to density consistently seen in all averages (white arrowheads).Parts of figures in Zhang et al. 1 published under a CC-BY license are reused.
For Warp reconstruction, the following steps f-j are not necessary.Warp makes use of the alignment parameters generated in Etomo up to this point.Proceed with the next steps for the first tilt series until the final tomogram is generated as a quality and consistency check.f.Skip the Tomogram Positioning step.Click ''Done'' to proceed.
Note: around X axis'' option under the Reorientation section.
This generates a text file recording the path and names of all deconvolved tomograms generated in previous steps by Warp.Protocolii.In the dcm GUI, click on Create binned versions under the Catalogue menu to pre-bin all tomograms with a factor of 2 for easier visualization (target voxel size: 6.78 3 2 2 = 27.10A˚)and filament tracing (will be annotated with the original 43 binned pixel size 6.78A ˚by default in the next step by Dynamo).iii.Select the first tomogram at its index field and right click to open the binned tomogram in tomoslice.iv.Follow the tutorial video (https://www.youtube.com/watch?v=hsZ-9xAYufI) to generate filament model.In brief: 5. Create filament models in Dynamo.Note: Our protocol was produced with an earlier version of Dynamo (1.1.401-foss-2017b-MCR-R2018a)together with MATLAB (2019b), but should be applicable to newer versions in most cases.We recommend to read the detailed instructions on usage of Dynamo in the software wiki (https://www.dynamo-em.org//w/index.php?title=Main_Page).a. Activate Dynamo in MATLAB with this command: b.Create and enter a new working directory to run the Dynamo project, e.g., ''4bin_dynamo''.c.Create a catalogue for 20 tomograms from this example data.i.In a terminal window (outside of MATLAB), run the command: Note: ii.Create a Dynamo catalogue named ''filaments'' by running: d.Generate filament models in the Dynamo graphical user interface (GUI).i. Open the Dynamo GUI with MATLAB command: >run /path-to-the-installation-folder-of-dynamo/dynamo/1.1.401-foss-2017b-MCR->dcm-create filaments -fromvll list_4b_deconv.vllSTAR Protocols 5, 103272, September 20, 2024 Here, reorder re-indexes particles after cropping because some positions too close to borders of tomograms will be skipped automatically by Dynamo.Also, a new table with final indices corresponding to the cropped particles is included in the particle directory.One can split the table based on tomogram indices later in MATLAB if needed.64 refers to the box size in 43 binned pixels (6.78A ˚/pix), which is about twice larger than the filament diameter measured from the tomograms.iv.Use this newly generated table (here renamed to: ''table_combined_reindex.tbl'') for Dynamo subtomogram averaging in next steps.1297 particles were generated in this step for the example dataset.
Dynamo tables and crop subtomograms from 43 binned tomograms with Dynamo commands in MATLAB.a.Follow the Dynamo wiki, and use the following command lines together with loop scripting to combine multiple model tables for each tomogram into one table per tomogram.>dcmfilamentsProtocolHere, the 1 after -i refers to the tomogram with index = 1 (also the first tomogram, explained in next steps).b.Crop particles in Dynamo for all tomograms together.i.Create a new volume list for cropping particles from the non-deconvolved tomograms in a terminal.ii.Modify this list file such that each tomogram gets an index number (1-20 in this case) and the absolute path and name of each table per tomogram in this format:iii.Crop all particles in MATLAB,Note:Note: If MATLAB is not available, most Dynamo commands can also be run directly in the terminal as a standalone version: with Dynamo loaded as a module or path defined, one can type ''dynamo'' in terminal to activate it and then run Dynamo functions in a slightly modified syntax (see Dynamo wiki for more details).>clear >ls -d /path-to-the-Warp-preprocessing-folder/frames/reconstruction/*.mrc>> list_4b.vll/path-to-the-Warp-preprocessing-folder/frames/reconstruction/*.mrc . Split the refined table from the project ''bin4_align_1'' into tables for individual filaments by reading the table into a variable and performing matrix operation in MATLAB based on the indices of tomograms in Column 20 of the table, or with the dynamo_table_grep function.c.Generate averages for individual tomograms from the split tables with the daverage and dwrite functions as shown above.One can automate and combine these two steps with a loop script in MATLAB.d.Inspect the averages for the individual filaments and takes note of the ones showing opposite directions to the first subtomogram average (used as a reference here).
>t=dread('refined_table_ref_001_ite_0004.tbl'); >t_double=t(:,1:26); >dwrite(t,'table_all.tbl') >dcp.new('abp_align','d','filamentsData','template','average_ref_001_ite_0004.em','masks','default','t','table_all.tbl');Note: 15. Run RELION 3D classification with helical reconstruction to check for heterogeneity.Settings are left as default, except for the changes in the following: a. Use the output STAR file from the 3D auto-refine job in step 14. b.Use the output average from the Post-processing job in step 14 as the reference map.c.Set up the RELION job in the GUI: i. Specify the STAR file, reference map, and the cylinder reference mask file (the same as in step 12) in the I/O tab.ii.In the Reference tab, leave the ''Ref.map is on absolute grayscale?''asYes.Set the Initial low-pass filter as 40 A ˚.iii.In the Optimisation tab, set the Number of classes as 4, the Mask diameter as 300 A ˚.iv.In the Sampling tab, set the Angular sampling interval as 1.8 degrees, the ''Perform local angular searches?''toYesanduse a Local angular search range of 10 degrees.v.In the Helix tab, change the following parameters: The job resulted in 4 classes, of which Class 3 contained 77.7% of the particles (939 particles) and had a resolution of 5.9 A ˚, refined helical twist of 27.17 degrees, rise of 4.21 A ˚. Class 4 contained 21.3% of the particles (258 particles) and had a resolution of 6.6 A ˚, refined helical twist of 26.93 degrees, rise of 3.51 A ˚.The other two classes containing 12 particles in total did not result in any meaningful averages.16.Run RELION 3D auto-refine with helical reconstruction for Classes 3 and 4 separately.Settings are similar to those in step 14, except for changes in the following: a. Use the STAR file for particles in Class 3 from the classification job, and that for Class 4 in a separate job.b.Use the output averages from the Classification job as Reference maps (averages from Class 3 and 4 separately for two jobs).c.Set up the RELION job in a GUI: i. Specify the STAR file, the Reference map, and the cylinder Reference mask file (the same as in step 12) in the I/O tab.ii.In the Reference tab, leave the ''Ref.map is on absolute grayscale?''asYes.Set the Initial low-pass filter as 20 A ˚.iii.In the Helix tab, change the following parameters: Note: The ''Keep tilt-prior fixed'' option is set to Yes in principle; if the RELION job aborts with the error ''Segmentation fault'', set this to No and re-run it.d.Run RELION Post-processing and inspect the results.Note: The resulting resolution for Class 3 was 6.5 A ˚with an Auto B-factor of À85 A ˚2. Re-doing helical search for the Class 3 average gives a helical parameter set of 27.17 degrees and 4.21 A ˚.The resolution for Class 4 is 7.3 A ˚with an Auto B-factor of À161 A ˚2. Re-doing helical search for the Class 4 average gives a helical parameter set of 26.91 degrees and 3.51 A ˚(final refinement maps of these two classes are shown in Figure Note: